DS007445: ieeg dataset, 19 subjects#

Thalamocortical ictal iEEG dataset

Access recordings and metadata through EEGDash.

Citation: Saarang Panchavati, Atsuro Daida, Sotaro Kanai, Shingo Oana, Hiroya Ono, Masaki Izumi, Kikuko Kaneko, Aria Fallah, Joe X Qiao, Noriko Salamon, Raman Sankar, Corey Arnold, William Speier, Hiroki Nariai (2026). Thalamocortical ictal iEEG dataset. 10.18112/openneuro.ds007445.v1.0.2

Modality: ieeg Subjects: 19 Recordings: 66 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007445

dataset = DS007445(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS007445(cache_dir="./data", subject="01")

Advanced query

dataset = DS007445(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds007445,
  title = {Thalamocortical ictal iEEG dataset},
  author = {Saarang Panchavati and Atsuro Daida and Sotaro Kanai and Shingo Oana and Hiroya Ono and Masaki Izumi and Kikuko Kaneko and Aria Fallah and Joe X Qiao and Noriko Salamon and Raman Sankar and Corey Arnold and William Speier and Hiroki Nariai},
  doi = {10.18112/openneuro.ds007445.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds007445.v1.0.2},
}

About This Dataset#

We investigated thalamocortical network dynamics using intracranial EEG (iEEG) recordings with thalamic sampling from 19 patients with focal epilepsy (1). The iEEG dataset analyzed in this study is publicly shared here. BIDS converstion was performed according to references (2) and (3). References (1) Panchavati S, Daida A, Kanai S, Oana S, Ono H, Izumi M, Kaneko K, Fallah A, Qiao JX, Salamon N, Sankar R, Arnold C, Speier W, Nariai H (2026). Distinct Spectral and Directional Thalamocortical Network Dynamics Define Focal Seizure Evolution. medRxiv, 2026 Feb 4:2026.02.03.26345480. doi: 10.64898/2026.02.03.26345480. (2) Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896 (3) Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D’Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7

Dataset Information#

Dataset ID	`DS007445`
Title	Thalamocortical ictal iEEG dataset
Author (year)	`Panchavati2026`
Canonical	—
Importable as	`DS007445`, `Panchavati2026`
Year	2026
Authors	Saarang Panchavati, Atsuro Daida, Sotaro Kanai, Shingo Oana, Hiroya Ono, Masaki Izumi, Kikuko Kaneko, Aria Fallah, Joe X Qiao, Noriko Salamon, Raman Sankar, Corey Arnold, William Speier, Hiroki Nariai
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007445.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 19
Recordings: 66
Tasks: 1

Channels & sampling rate

Channels: 140 (10), 138 (10), 83 (6), 265 (6), 202 (5), 216 (5), 162 (4), 203 (3), 112 (3), 68 (2), 49 (2), 81 (2), 263, 120, 201, 139, 111, 124, 261, 137
Sampling rate (Hz): 200.0 (42), 2000.0 (17), 200.00000000000003 (6), 1999.9999999999998
Duration (hours): 73.70927875000001

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Age distribution (n=1, range 73–73 yr)

70

Channel counts (ch)

49688183111112120124137138139140162201202203216261263265

Sampling frequencies (Hz)

200200.02000.02000

Total recording duration: 73 h

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS007445 class to access this dataset programmatically.

class eegdash.dataset.DS007445(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Thalamocortical ictal iEEG dataset

Study:: ds007445 (OpenNeuro)
Author (year):: Panchavati2026
Canonical:: —

Also importable as: DS007445, Panchavati2026.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 19; recordings: 66; tasks: 1.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007445 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007445 DOI: https://doi.org/10.18112/openneuro.ds007445.v1.0.2

Examples

>>> from eegdash.dataset import DS007445
>>> dataset = DS007445(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)